LEVELNET to visualize, explore, and compare protein-protein interaction networks

Comprehensive profiling of candidate biomarkers and immune infiltration landscape in metabolic dysfunction-associated steatohepatitis

Background

The incidence of metabolic dysfunction-associated steatohepatitis (MASH) is increasing, with an incompletely understood pathophysiology involving multiple factors, particularly innate and adaptive immune responses. Given the limited pharmacological treatments available, identification of novel immune metabolic targets is urgently needed. In this study, we aimed to identify hub immune-related genes and potential biomarkers with diagnostic and predictive value for MASH patients.

Methods

The GSE164760 dataset from the Gene Expression Omnibus was utilized for analysis, and the R package was used to identify differentially expressed genes. Immune-related differentially expressed genes (IR-DEGs) were identified by comparing the overlap of differentially expressed genes with well-known immune-related genes. Furthermore, the biological processes and molecular functions of the IR-DEGs were analyzed. To characterize the hub IR-DEGs, we employed a protein-protein interaction network. The diagnostic and predictive values of these hub IR-DEGs in MASH were confirmed using GSE48452 and GSE63067 datasets. Finally, the significance of the hub IR-DEGs was validated using a mouse model of MASH.

Results

A total of 91 IR-DEGs were identified, with 61 upregulated and 30 downregulated genes. Based on the protein-protein interaction network, FN1, RHOA, FOS, PDGFRα, CCND1, PIK3R1, CSF1, and FGF3 were identified as the hub IR-DEGs. Moreover, we found that these hub genes are closely correlated with immune cells. Notably, the validation across two independent cohorts as well as a murine MASH model confirmed their high diagnostic potential.

Conclusion

The hub IR-DEGs, such as FN1, RHOA, FOS, PDGFRα, CCND1, PIK3R1, CSF1, and FGF3, may enhance the diagnosis and prognosis of MASH by modulating immune homeostasis.

Connecting the dots: Computational network analysis for disease insight and drug repurposing

Network biology is a powerful framework for studying the structure, function, and dynamics of biological systems, offering insights into the balance between health and disease states. The field is seeing rapid progress in all of its aspects: data availability, network synthesis, network analytics, and impactful applications in medicine and drug development. We review the most recent and significant results in network biomedicine, with a focus on the latest data, analytics, software resources, and applications in medicine. We also discuss what in our view are the likely directions of impactful development over the next few years.

PPI3D: a web server for searching, analyzing and modeling protein-protein, protein-peptide and protein-nucleic acid interactions

Structure-resolved protein interactions with other proteins, peptides and nucleic acids are key for understanding molecular mechanisms. The PPI3D web server enables researchers to query preprocessed and clustered structural data, analyze the results and make homology-based inferences for protein interactions. PPI3D offers three interaction exploration modes: (i) all interactions for proteins homologous to the query, (ii) interactions between two proteins or their homologs and (iii) interactions within a specific PDB entry. The server allows interactive analysis of the identified interactions in both summarized and detailed manner. This includes protein annotations, structures, the interface residues and the corresponding contact surface areas. In addition, users can make inferences about residues at the interaction interface for the query protein(s) from the sequence alignments and homology models. The weekly updated PPI3D database includes all the interaction interfaces and binding sites from PDB, clustered based on both protein sequence and structural similarity, yielding non-redundant datasets without loss of alternative interaction modes. Consequently, the PPI3D users avoid being flooded with redundant information, a typical situation for intensely studied proteins. Furthermore, PPI3D provides a possibility to download user-defined sets of interaction interfaces and analyze them locally. The PPI3D web server is available at https://bioinformatics.lt/ppi3d.